Synapses of the mammalian central nervous system (CNS) are very deeply diverse in both molecular and functional properties. At present, unfortunately, our understanding of this diversity is rudimentary, and quantitative data on the subject are very few. Left unfathomed, CNS synapse diversity poses formidable obstacles to better understanding of the development, function and disorders of the brain's synaptic circuitry. The major reason for the persistence of this distressing state of ignorance lies in the fact that tools for exploring synapse populations at the level of individual synapses are few and limited in their capabilities. To address the challenges synapse diversity poses to both basic and clinical neuroscience, this project aims to develop a superlative new proteometric imaging platform capable of analyzing very large synapse populations in situ with single- synapse resolution. Deployment and dissemination of this platform will facilitate study and treatment of the many neurodevelopmental, mental and neurodegenerative disorders linked to specific synapse subpopulations, as well as opening new perspectives on molecular mechanisms, circuit architectures and disorders of CNS memory encoding, storage and retrieval. The platform will be based on immunofluorescence array tomography (IAT) and involve development of novel antibody standardization protocols and novel image acquisition hardware and software. These innovations will improve the reproducibility, quantitative reliability, and speed of IAT by large margins and overcome limitations that have so far prevented proteometric analysis of large synapse populations at the single-synapse level. The platform would be capable of proteometric census of a million of more synapses per hour, at 50 or more markers per synapse, while maintaining precise neuroanatomical and molecular coordinates for each synapse. The new platform will be demonstrated by a 7-marker proteometric survey of an adult mouse cortex barrel column that would enumerate each of the tens of millions of synapses in the column and allow classification of each synapse based on neurotransmitter type and a set of neurons type markers. The results will be disseminated via methods publications, open-source